search cancel | B2B Software for the Real Estate Industry


Quick Information

  • Name of Startup:
  • Year Founded: 2023
  • Website:
  • Type of company: B2B Software for the Real Estate Industry
  • HQ Location: Delware

Startup Founders:

Nicolas Lassaux | Founder & CEO |

Startup one-liner:

APIs to extract, enrich & predict with real estate data

Problem the startup solves:

  • AnyExtract – Unlock Valuable Data from Real Estate Documents
  • CurbScore. – Quantify the Curb Appeal of your Real Estate Investment
  • FloorPlans – Extract Structured Data from Multifamily Floor Plans
  • LiquidRent – Optimize Revenue for any Property Management Software
  • RealType – Write Real Estate Listing Descriptions in Seconds
  • RentSource – Add Rent, Amenity and Concessions Data to Your Pipeline

Progress and Current Status:

We have several clients and about $500k in annual revenue.

What is a Crazy Story about the Startup?:

After working in Data Science for Real Estate for years, I initially used my knowledge to solve my housing problems, and since I had some time to R&D, that eventually became something much bigger:

FloorPlansAI: It began when I tried to extract structured data from apartment floor plans to find one that met my fiancé and my expectations in a competitive housing market with awkward floor plans (we didn’t want the main bedroom to be a narrow corridor, a common feature in Amsterdam). With this floor plan extraction algorithm, we could filter every single new listing reaching the market in less than 100ms, and only focus on those meeting our criteria.

CurbScoreAI: Of course, we also wanted to live in a nice area. So for each potential apartment, I used Deep Learning and Computer Vision to rank the view from the street in terms of curb appeal. To achieve this, I scored streets and facades based on maintenance, style, and other factors such as the presence of trees. After refining it, the CurbScoreAI algorithm can now be computed for any building in any city in the US.

RentSourceAI and AnyExtractAI: After I had a short list of apartments, I wanted to collect more data on each apartment from property websites and documents (PDFs) to get a more complete picture about each place – without having to label thousands of documents and not only return fields, but structured answers with hierarchies of objects, lists, etc., which is where current extraction platforms fail.

All that gathered data was ideal for analyzing how those detailed characteristics worked together. How much are corridor square feet worth compared to those of a larger bedroom? Does any room layout offer an unfair advantage and generate more demand than others? Are there good practices for writing listing descriptions? I could answer these questions using the technology that had already been built!

RealTypeAI: The arrival of GPT-3 and better large language models at that time was pure luck. I could already extract unprecedented detailed data about houses and apartments, and had seen how bad the average listing description was: typos, non-relevant emphasis, missed important points about some amenities or neighborhood characteristics. With that data and GPT-3 capabilities, I figured the problem of writing good listing descriptions could be solved. By overcoming GPT-3 weaknesses with good data and some guidance, it was possible to generate listing descriptions so well that most people I showed preferred the AI-generated ones to the real thing.

LiquidRentAI: About improving listings… the elephant in the room was getting the pricing right. With the ability to view all the data in a structured manner and estimate supply and demand, building rent adjustment recommendations using those signals was the natural next step. LiquidRentAI is a custom-built numerical simulation for each of your buildings. It’s built so that you can play with rents and see how it impacts the whole funnel. Then it finds the best tradeoff to maximize total income. It couldn’t be more transparent.

What is a Company the Startup Looks Up to, and Why?:

We’re big fan’s of Netflix because of their renowned culture of freedom and responsibility. When you hire talented people and trust your team to do the right thing, everything is easier and more efficient. We love “No Rules Rules” and “That Will Never Work” and have recommended them to many friend and coworkers.

The Company in Four Years Will Be…

In four years, HelloDataAI will be the market leader in real estate data extraction, enrichment and predictive analytics. We focus on solving specific, high-impact problems in the real estate space and build APIs that can be easily integrated into existing data pipelines. Customers love working with our talented engineering team because we not only provide incredible technology, but help them think differently about their business to improve outcomes.


Author : Tom Heien

Admin at KillerStartups. Loves to talk about artificial intelligence, automation, and the future of the internet.

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